Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

نویسندگان

چکیده

There are more than 962 million people aged 60 and up globally. Physical activity declines as get older, does their capacity to undertake everyday tasks, effecting both physical mental health. Many researchers use machine learning deep methods recognize human activities, but very few studies have been focused on recognition of elderly people. This paper focuses providing assistance by monitoring activities in different indoor outdoor environments using gyroscope accelerometer data collected from a smart phone. Smart phones routinely used monitor the persons with impairments; routine such sitting, walking, going upstairs, downstairs, standing, lying included dataset. Conventional Machine Learning Deep algorithms k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, Long Short-Term Memory Network for recognition. is recurrent neural network variation that best suited handling temporal sequences. Two-fold ten-fold cross-validation were performed show effect changing training testing Among all classification techniques, proposed gave accuracy 95.04%. However, 89.07% low computational time 0.42 min 10-fold cross-validation.

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ژورنال

عنوان ژورنال: Information

سال: 2022

ISSN: ['2078-2489']

DOI: https://doi.org/10.3390/info13060275